Update app.py

app.py

@@ -2,15 +2,12 @@ import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
 model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
 
-# Set the model to evaluation mode
 model.eval()
 
 def predict_sentiment(text):
-    # Tokenize the input text and prepare it to be used as model input
     inputs = tokenizer.encode_plus(
         text,
         add_special_tokens=True,
@@ -19,12 +16,10 @@ def predict_sentiment(text):
         truncation=True
     )
 
-    # Perform the prediction
     with torch.no_grad():
         outputs = model(**inputs)
         predicted_class = torch.argmax(outputs.logits, dim=1).item()
     
-    # Determine the sentiment based on the predicted class
     if predicted_class == 0:
         return "Negative sentiment"
     elif predicted_class == 1:
@@ -35,7 +30,7 @@ def predict_sentiment(text):
 # Streamlit interface
 st.title("Financial News Sentiment Analysis")
 
-user_input = st.text_area("Enter the financial news text:")
+user_input = st.text_area("Made by Ahmad Moiz with Love","Enter the financial news text:")
 if st.button("Analyze"):
     if user_input:
         # Get the sentiment prediction